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Recent graph-to-text models generate text from graph-based data using eitherglobal or local aggregation to learn node representations. Global node encodingallows explicit communication between two distant nodes, thereby neglectinggraph topology as all nodes are directly connected. In contrast, local nodeencoding considers the relations between neighbor nodes capturing the graphstructure, but it can fail to capture long-range relations. In this work, wegather both encoding strategies, proposing novel neural models which encode aninput graph combining both global and local node contexts, in order to learnbetter contextualized node embeddings. In our experiments, we demonstrate thatour approaches lead to significant improvements on two graph-to-text datasetsachieving BLEU scores of 18.01 on AGENDA dataset, and 63.69 on the WebNLGdataset for seen categories, outperforming state-of-the-art models by 3.7 and3.1 points, respectively.

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs